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DOI: 10.4230/LIPIcs.CCC.2026.1
Tight Lower Bound for Approximating Parametrized Maximum Likelihood Decoding Under ETH

Authors: Rishav Gupta, Bingkai Lin, and Xin Zheng

Published in: LIPIcs, Volume 383, 41st Computational Complexity Conference (CCC 2026)

Abstract
We present a simple deterministic reduction which, assuming the Exponential Time Hypothesis (ETH), yields tight lower bounds for approximating the parameterized Maximum Likelihood Decoding problem (MLD) and the parameterized Nearest Codeword Problem (NCP) within some fixed constant factor. Our starting point is the ETH-based exponential-time hardness of (c, s)-Gap MAXLIN established in [Nir Bitansky et al., 2024]. We transform a (c, s)-Gap MAXLIN instance into an instance of γ-Gap k-MLD via a novel combinatorial object that we call a cover family. We provide both a randomized construction of the required cover families and a subsequent derandomization. Prior to our work, n^{Ω(k)} hardness for constant-factor approximation was only shown under the randomized Gap Exponential Time Hypothesis Gap-ETH [Pasin Manurangsi, 2020], which is a much stronger assumption than ETH. Under ETH, the strongest known lower bound was n^{Ω(k/poly log k)} due to [Mitali Bafna et al., 2025]. Unlike previous approaches that rely on reductions from the hardness of approximating 2-CSP, our reduction provides a more direct and conceptually simpler route to achieving the optimal lower bounds.
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Rishav Gupta, Bingkai Lin, and Xin Zheng. Tight Lower Bound for Approximating Parametrized Maximum Likelihood Decoding Under ETH. In 41st Computational Complexity Conference (CCC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 383, pp. 1:1-1:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{gupta_et_al:LIPIcs.CCC.2026.1,
  author =	{Gupta, Rishav and Lin, Bingkai and Zheng, Xin},
  title =	{{Tight Lower Bound for Approximating Parametrized Maximum Likelihood Decoding Under ETH}},
  booktitle =	{41st Computational Complexity Conference (CCC 2026)},
  pages =	{1:1--1:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-437-6},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{383},
  editor =	{Moshkovitz, Dana},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2026.1},
  URN =		{urn:nbn:de:0030-drops-270439},
  doi =		{10.4230/LIPIcs.CCC.2026.1},
  annote =	{Keywords: Maximum Likelihood Decoding, Parameterized Complexity, Hardness of Approximation, Exponential Time Hypothesis}
}
Document
DOI: 10.4230/LIPIcs.STACS.2025.65
On Average Baby PIH and Its Applications

Authors: Yuwei Liu, Yijia Chen, Shuangle Li, Bingkai Lin, and Xin Zheng

Published in: LIPIcs, Volume 327, 42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025)

Abstract
The Parameterized Inapproximability Hypothesis (PIH) asserts that no FPT algorithm can decide whether a given 2CSP instance parameterized by the number of variables is satisfiable, or at most a constant fraction of its constraints can be satisfied simultaneously. In a recent breakthrough, Guruswami, Lin, Ren, Sun, and Wu (STOC 2024) proved the PIH under the Exponential Time Hypothesis (ETH). However, it remains a major open problem whether the PIH can be established assuming only W[1]≠FPT. Towards this goal, Guruswami, Ren, and Sandeep (CCC 2024) showed a weaker version of the PIH called the Baby PIH under W[1]≠FPT. In addition, they proposed one more intermediate assumption known as the Average Baby PIH, which might lead to further progress on the PIH. As the main contribution of this paper, we prove that the Average Baby PIH holds assuming W[1]≠FPT. Given a 2CSP instance where the number of its variables is the parameter, the Average Baby PIH states that no FPT algorithm can decide whether (i) it is satisfiable or (ii) any multi-assignment that satisfies all constraints must assign each variable more than r values on average for any fixed constant r > 1. So there is a gap between (i) and (ii) on the average number of values that are assigned to a variable, i.e., 1 vs. r. If this gap occurs in each variable instead of on average, we get the original Baby PIH. So central to our paper is an FPT self-reduction for 2CSP instances that turns the above gap for each variable into a gap on average. By the known W[1]-hardness for the Baby PIH, this proves that the Average Baby PIH holds under W[1] ≠ FPT. As applications, we obtain (i) for the first time, the W[1]-hardness of constant approximating k-ExactCover, and (ii) a tight relationship between running time lower bounds in the Average Baby PIH and approximating the parameterized Nearest Codeword Problem (k-NCP).
Cite as

Yuwei Liu, Yijia Chen, Shuangle Li, Bingkai Lin, and Xin Zheng. On Average Baby PIH and Its Applications. In 42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 327, pp. 65:1-65:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{liu_et_al:LIPIcs.STACS.2025.65,
  author =	{Liu, Yuwei and Chen, Yijia and Li, Shuangle and Lin, Bingkai and Zheng, Xin},
  title =	{{On Average Baby PIH and Its Applications}},
  booktitle =	{42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025)},
  pages =	{65:1--65:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-365-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{327},
  editor =	{Beyersdorff, Olaf and Pilipczuk, Micha{\l} and Pimentel, Elaine and Thắng, Nguy\~{ê}n Kim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2025.65},
  URN =		{urn:nbn:de:0030-drops-228900},
  doi =		{10.4230/LIPIcs.STACS.2025.65},
  annote =	{Keywords: Average Baby PIH, Parameterized Inapproximability, Constraint Satisfaction Problem, Exact Set Cover, W\lbrack1\rbrack-hardness}
}
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